Performance Comparison of Naive Bayes and Support Vector Machine Methods in Music Genre Classification Based on Audio Signal Feature Extraction Using Mel-Frequency Cepstral Coefficients (MFCC) - Naserwan, Kevin Putrayudha; Miraswan, Kanda Januar; Utari, Meylani (Politeknik Negeri Batam, 2026)
Music genre classification has gained increasing attention with the emergence of digital music platforms. One of the relevant features extracted from audio signals is Mel-Frequency Cepstral Coefficients (MFCC), which is widely recognized as an effective technique. MFCC features are extracted at the frame level and aggregated at the clip level to represent each music track, making them suitable for audio-based classification tasks. This study applies Naïve Bayes and Support Vector Machine (SVM) algorithms for classification using the GTZAN dataset consisting of 1,000 audio files from 10 music genres, each with a duration of 30 seconds. The performance of these methods is evaluated using accuracy, precision, recall, and F1-score. The results show that SVM demonstrates superior performance, achieving an accuracy of 95.25% compared to 50.37% for Naïve Bayes. This performance gap can be attributed to SVM’s ability to model non-linear decision boundaries and effectively handle high-dimensional MFCC feature spaces. The main contribution of this study lies in the systematic evaluation of multiple SVM kernel configurations and parameter settings, providing empirical insights into the robustness of classical machine learning methods for MFCC-based music genre classification. This study concludes that SVM is better than Naive Bayes in music genre classification with MFCC feature extraction.